This paper presents a concise overview of the application of machine learning (ML) techniques in managing water resources systems, highlighting their potential to optimize operations, enhance decision-making, and address the growing complexities of water management. The study aims to bridge the gap between traditional management practices and modern computational approaches by exploring how ML methods can model hydrological processes, predict water demand, assess water quality, and support sustainable resource allocation. The objective is to examine the capabilities of ML algorithms, such as neural networks, support vector machines, and ensemble learning models, in addressing challenges like climate variability, population growth, and data scarcity. The review synthesizes recent advancements and showcases case studies demonstrating the integration of ML in real-world scenarios. By identifying current limitations and future research directions, the study provides a foundational understanding for stakeholders to leverage ML tools for effective, adaptive, and resilient water resource management.